An Accurate and Practical Explicit Hybrid Method for the Chan–Vese Image Segmentation Model

In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan–Vese functional for image segmentation. The resulting governing equation is the Allen...

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Bibliographic Details
Published inMathematics (Basel) Vol. 8; no. 7; p. 1173
Main Authors Jeong, Darae, Kim, Sangkwon, Lee, Chaeyoung, Kim, Junseok
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2020
Subjects
Algorithms
Allen–Cahn equation
finite difference method
Gradient flow
image processing
Image segmentation
License plates
Mathematics
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Summary:In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan–Vese functional for image segmentation. The resulting governing equation is the Allen–Cahn equation with a nonlinear fidelity term. We numerically solve the equation by employing an operator splitting method. We use two closed-form solutions and one explicit Euler’s method, which has a mild time step constraint. However, the proposed scheme has the merits of simplicity and versatility for arbitrary computational domains. We present computational experiments demonstrating the efficiency of the proposed method on real and synthetic images.
ISSN:2227-7390
2227-7390
DOI:10.3390/math8071173